1. Create a Forecast:

Take data, create forecast based on the classical decomposition example, except monthly data instead of quarterly.

For help review Exercise 6: Exercise 6 is based on Quarterly data. Kianas is creating a monthly forecast based on monthly data. You will create a seasonal Index for each month.

This forecast is considered average demand.

Regression Output Time period 1 2 3 4 5 6 7 8 9 10 11 12 Deseasonalized forecast Seasonalized forecast 13 14 15 16 17 Month Demand Yearly average Seasonal Index Average Si Deseasonalized 1/1/17 2401 2/1/17 2553 3/1/17 2566 4/1/17 2980 5/1/17 3565 6/1/17 3621 7/1/17 2201 8/1/17 2352.5 9/1/17 2409.5 10/1/17 2719 11/1/17 3567 12/1/17 3106 1/1/18 2608 2/1/18 2486.5 3/1/18 2641.5 4/1/18 3249 5/1/18 3745 6/1/18 3563 7/1/18 2643 8/1/18 3048 9/1/18 3074 10/1/18 3622 11/1/18 4328 12/1/18 4307 1/1/19 2793 2/1/19 2972.5 3/1/19 3029.5 4/1/19 3627 5/1/19 3693 6/1/19 3922 7/1/19 2939 8/1/19 3329 9/1/19 3390 10/1/19 4359 11/1/19 5079.5 12/1/19 5086.5 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Regression Output Time period 1 2 3 4 5 6 7 8 9 10 11 12 Deseasonalized forecast Seasonalized forecast 13 14 15 16 17 Month Demand Yearly average Seasonal Index Average Si Deseasonalized 1/1/17 2401 2/1/17 2553 3/1/17 2566 4/1/17 2980 5/1/17 3565 6/1/17 3621 7/1/17 2201 8/1/17 2352.5 9/1/17 2409.5 10/1/17 2719 11/1/17 3567 12/1/17 3106 1/1/18 2608 2/1/18 2486.5 3/1/18 2641.5 4/1/18 3249 5/1/18 3745 6/1/18 3563 7/1/18 2643 8/1/18 3048 9/1/18 3074 10/1/18 3622 11/1/18 4328 12/1/18 4307 1/1/19 2793 2/1/19 2972.5 3/1/19 3029.5 4/1/19 3627 5/1/19 3693 6/1/19 3922 7/1/19 2939 8/1/19 3329 9/1/19 3390 10/1/19 4359 11/1/19 5079.5 12/1/19 5086.5 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36